AUTONOMOUS MULTI-AGENT SYSTEMS FOR INTELLIGENT DISASTER RESPONSE: A SURVEY OF AI-DRIVEN COORDINATION AND DECISION-MAKING

Natural and human-made disasters demand rapid, coordinated, and adaptive response strategies to minimize loss of life and infrastructure damage. Traditional disaster management systems often struggle with delayed information processing, limited situational awareness, and inefficient resource allocation. Autonomous Multi-Agent Systems (MAS), supported by Artificial Intelligence (AI), offer a decentralized approach in which multiple intelligent agents collaborate to monitor environments, analyze data, allocate resources, and coordinate emergency operations. This paper reviews the architecture, communication models, applications, benefits, implementation challenges, and future prospects of AI-driven Multi-Agent Systems for disaster response. The study also examines the integration of MAS with unmanned aerial vehicles (UAVs), Internet of Things (IoT), edge computing, blockchain, and digital communication networks. The findings indicate that intelligent multi-agent coordination can significantly improve disaster preparedness, emergency response efficiency, and post-disaster recovery through real-time collaboration and autonomous decision-making.